Optical Character Recognition (OCR) generally refers is the mechanism of converting images of typed, handwritten or printed text into machine-encoded text (e.g., American Standard Code for Information Interchange (ASCII)), whether from a scanned document, a photo of a document, a scene-photo (e.g., an image acquired from a surveillance camera including a license plate number) or from subtitle text in an image (e.g., closed captioning text). Generally, an OCR mechanism is a computer-implemented process that includes the steps of acquiring an image containing a string of characters to be recognized, recognizing individual characters in the input image as characters of an alphabet, segmenting the characters into one or more strings of characters, performing a string recognition mechanism to return a corresponding output string of characters that corresponds to one or more model strings that are searches in the image (e.g., license plate, serial numbers, postal codes, addresses, etc.).
OCR has a wide range of applications including the recognition of vehicle license plate numbers (e.g., for use in automated traffic law enforcement, surveillance, access control, tolls, etc.), the recognition of serial numbers on parts in an automated manufacturing environment, the recognition of labels on packages (e.g., pharmaceutical packaging, food and beverage packaging, household and personal products packaging, etc.), and various document analysis applications.
Despite sophisticated OCR techniques, OCR errors frequently occur due to the non-ideal conditions of image acquisition, the partial occlusion or degradation of the depicted characters, and especially the structural similarity between certain characters (e.g. Z and 2, 0 and D, 1 and I). For example, the recognition of vehicle license plate numbers must overcome lighting conditions that are both variable (according to the time of day, weather conditions, etc.) and non-uniform (e.g. due to shadows and specular reflection), perspective distortion, and partial occlusion or degradation of the characters (e.g. due to mud, wear of the paint, etc.).
Further the use of OCR in new fields (recognition of vehicle license plate numbers, serial numbers on parts in an automated manufacturing environment, the recognition of labels on packages, etc.) introduces new challenges and complexity as the images analyzed may include a significant amount of noise that need to be filtered in order to extract the relevant text. In particular, the images may include additional texts that can erroneously be identified as the searched text.